Validation of a Mobile, Sensor-based Neurobehavioral Assessment With Digital Signal Processing and Machine-learning Analytics

Abstract

Background: The Miro Health Mobile Assessment Platform consists of self-administered neurobehavioral and cognitive assessments that measure behaviors typically measured by specialized clinicians.

Objective: To evaluate the Miro Health Mobile Assessment Platform's concurrent validity, test-retest reliability, and mild cognitive impairment (MCI) classification performance.

Method: Sixty study participants were evaluated with Miro Health version V.2. Healthy controls (HC), amnestic MCI (aMCI), and nonamnestic MCI (naMCI) ages 64-85 were evaluated with version V.3. Additional participants were recruited at Johns Hopkins Hospital to represent clinic patients, with wider ranges of age and diagnosis. In all, 90 HC, 21 aMCI, 17 naMCI, and 15 other cases were evaluated with V.3. Concurrent validity of the Miro Health variables and legacy neuropsychological test scores was assessed with Spearman correlations. Reliability was quantified with the scores' intraclass correlations. A machine-learning algorithm combined Miro Health variable scores into a Risk score to differentiate HC from MCI or MCI subtypes.

Results: In HC, correlations of Miro Health variables with legacy test scores ranged 0.27-0.68. Test-retest reliabilities ranged 0.25-0.79, with minimal learning effects. The Risk score differentiated individuals with aMCI from HC with an area under the receiver operator curve (AUROC) of 0.97; naMCI from HC with an AUROC of 0.80; combined MCI from HC with an AUROC of 0.89; and aMCI from naMCI with an AUROC of 0.83.

Conclusion: The Miro Health Mobile Assessment Platform provides valid and reliable assessment of neurobehavioral and cognitive status, effectively distinguishes between HC and MCI, and differentiates aMCI from naMCI.